Recognition of Design Fixation via Body Language Using Computer Vision

Zhongliang Yang,Song Zhang,Yumiao Chen,Kai Zhang

doi:10.1155/2021/6649300

Abstract

The main objective of this study is to recognize design fixation accurately and effectively. First, we conducted an experiment to record the videos of design process and design sketches from 12 designers for 15 minutes. Then, we executed a video analysis of body language in designers, correlating body language to the presence of design fixation, as judged by a panel of six experts. We found that three body language types were significantly correlated to fixation. A two-step hybrid recognition model of design fixation based on body language was proposed. The first-step recognition model of body language using transfer learning based on a pretrained VGG-16 convolutional neural network was constructed. The average recognition rate achieved by the VGG-16 model was 92.03%. Then, the frames of recognized body language were used as input vectors to the second-step fixation classification model based on support vector machine (SVM). The average recognition rate for the fixation state achieved by the SVM model was 79.11%. The impact of the work could be that the fixation can be detected not only by the sketch outcomes but also by monitoring the movements, expressions, and gestures of designers, as it is happening by monitoring the movements, expressions, and gestures of designers.

Highlights

Administrator, professionals, teaching staff, and statesmen have indicated that making innovations is the key to our future [1]
Our body languagebased method addresses these limitations, and we propose a two-step hybrid model for design fixation classification on the foundation of the previous research and advanced convolutional neural network (CNN)
Mapping the feature variables of the original data to these two components for dimensionality reduction can reduce the dimension of features, and retain most of the information in the original data. e composition of feature variables after dimension reduction is shown in Table 10. e features presented by this matrix showed that B4 had the highest component 1, which was similar to the features presented in Figure 13. e “others,” including more body language, came in a close second place to B7

Summary

Introduction

Administrator, professionals, teaching staff, and statesmen have indicated that making innovations is the key to our future [1]. E focus of innovation is always on generating new ideas of products and/or services by designers [3]. Researchers around the world are studying the causes, influencing factors, effects, and indicators of design fixation [5, 8]. Design fixation is hard to combat because it occurs unconsciously [7]. Designers, even those who study and teach design regularly, do not know when they are being influenced or fixated by inadequate or misleading information [8]. Existing literature provides very limited insights regarding how to recognize fixation effectively. Based on these concerns, this study will focus on the recognition of design

Results

Discussion

Conclusion